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Computer Science Phd Research Intern Jobs in New York

Modal, Inc

ML Research Intern

New York, NY · On-site

Currently pursuing a PhD in computer science, machine learning, or a related field. * A demonstrated record of research in reinforcement learning, machine learning, foundation models, or related ...

Haize Labs

Research Intern

Manhattan, NY · On-site

$125K - $200K/yr

Research Intern Haize Labs haizes LLMs at scale. We are the robustness layer eliminating the risk ... We are turning down our Stanford PhD offers to send it on Haize Labs, have gotten into Y Combinator ...

Point72

Quantitative Research Intern

New York, NY · On-site

This is an opportunity for students and researchers of advanced data modeling and statistical ... MS, or PhD candidates in finance, computer science, mathematics, physics, or other quantitative ...

Mercor

ML Research Scientist - PhD

New York, NY · Remote

$110 - $145/hr

ML Research PhD Experts (ICML / NeurIPS / ICLR Publications) Type: Contract Compensation: $110-$145 ... PhD in Machine Learning , Computer Science , AI , or a closely related field. * Published at least ...

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Computer Science Phd Research Intern information

What does a computer science PhD research intern do?

A Computer Science PhD Research Intern typically contributes to cutting-edge research projects under the guidance of experienced mentors, often in an academic or industry setting. Responsibilities may include designing experiments, developing algorithms, conducting data analysis, and publishing findings in conferences or journals. The role is designed to provide practical research experience, sharpen technical skills, and foster collaboration with other researchers. Interns often have the opportunity to work on real-world problems and gain insights into the research and development process within the field of computer science.

What types of projects and collaborations can a computer science PhD research intern typically expect during their internship?

As a Computer Science PhD Research Intern, you will often work on advanced research projects that align with your academic interests and the organization's strategic goals. Interns typically collaborate closely with senior researchers, engineers, and sometimes cross-functional teams such as product or data science groups. Projects may involve designing experiments, developing prototypes, or publishing findings in academic venues. The environment is usually supportive of exploration and peer feedback, and you’ll have opportunities to present your work, gain mentorship, and contribute to impactful, real-world applications.

What are the key skills and qualifications needed to thrive as a computer science PhD research intern, and why are they important?

To thrive as a Computer Science PhD Research Intern, you need advanced knowledge in computer science fundamentals, research methodology, and typically be enrolled in a relevant PhD program. Experience with programming languages (such as Python, C++, or Java), data analysis tools, and version control systems like Git is often required. Strong analytical thinking, problem-solving abilities, and effective communication skills help interns collaborate and present complex ideas clearly. These skills are essential for contributing to innovative research projects, publishing findings, and succeeding in a competitive academic or industry research environment.

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Computer Science Phd Research Intern jobs near you
Infographic showing various Computer Science Phd Research Intern job openings in New York as of August 2026, with employment types broken down into 1% As Needed, 86% Full Time, 10% Part Time, 2% Contract, and 1% Nights. Highlights an 81% Physical, 2% Hybrid, and 17% Remote job distribution.

ML Research Intern

Modal, Inc

New York, NY • On-site

Full-time

Re-posted 4 days ago

Job description

About Us:
AI needs a new infrastructure layer. We're building it at Modal.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.
Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
The Role:
We are looking for PhD research interns with strong research experience in reinforcement learning, machine learning, and foundation models, including large language and multimodal models, to join our research team. This internship is well suited to candidates interested in improving existing methods and developing new techniques for large-scale model training, optimization, and inference, extending models to long-context and long-horizon tasks, and improving inference-time efficiency, reliability, and robustness in high-stakes real-world deployments.
Preferred Qualifications:
  1. Currently pursuing a PhD in computer science, machine learning, or a related field.
  2. A demonstrated record of research in reinforcement learning, machine learning, foundation models, or related areas.
  3. Experience developing and evaluating large-scale models or machine learning systems.
  4. Familiarity with distributed training, large-scale inference, or multi-GPU environments.
  5. Publications at leading venues such as NeurIPS, ICML, ICLR, CVPR, CoRL, UAI, JMLR, or TMLR.
  6. Strong programming and engineering skills, with the ability to translate research ideas into working implementations.
  7. A collaborative, mission-driven mindset and the ability to work effectively across research and engineering teams.

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